Microarray analysis reveals altered expression of multiple circular RNAs in the pathogenesis of esophageal squamous cell carcinoma

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1 Original Article Microarray analysis reveals altered expression of multiple circular RNAs in the pathogenesis of esophageal squamous cell carcinoma Hao Chen, Bin Zheng, Lin Huang, Wei Zheng, Chun Chen Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou 351, China Contributions: (I) Conception and design: C Chen; (II) Administrative support: C Chen; (III) Provision of study materials or patients: W Zheng, B Zheng; (IV) Collection and assembly of data: H Chen; (V) Data analysis and interpretation: H Chen, L Huang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. Correspondence to: Chun Chen. Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou 351, China. chenchun29@163.com. Background: Esophageal carcinoma remains one of the most prevalent malignancies worldwide and has a poor prognosis. Esophageal squamous cell carcinoma (ESCC) is the predominant type of esophageal carcinoma in Eastern Asia. The predilection site and biological behavior of ESCC differ from those of esophageal adenocarcinoma among patients in Western countries, indicating that ESCC has a distinctive oncogenesis mechanism. There are only a few studies reporting the role of circular RNAs (circrnas) in the molecular mechanisms of ESCC. The purpose of this study was to identify the specific circrnas involved in ESCC pathogenesis. Methods: Cancerous tissues were acquired from surgical specimens from five ESCC patients, and adjacent non-neoplastic tissues were also collected as controls. All samples were analyzed by Arraystar Human circrna Arrays. Differentially expressed circrnas between two groups were identified by bioinformatics analysis, and seven circrnas were randomly selected for further validation by qrt-pcr. circrna/ microrna interactions were predicted with an in-house mirna target prediction software from Arraystar. Results: Compared with the adjacent non-neoplastic tissues, 267 circrnas were significantly differentially expressed in ESCC tissues (fold change >1.5, P value <.5). Among the seven selected circrnas, the microarray data for five circrnas were validated by qrt-pcr two were upregulated (hsa_ circrna_11125 and hsa_circrna_6826), and three were downregulated (hsa_circrna_11, hsa_ circrna_13836, and hsa_circrna_11839). Finally, potential target mirnas for the five confirmed circrnas were predicted. Conclusions: Our study demonstrates that multiple differentially expressed circrnas are involved in the process of ESCC pathogenesis. Hsa_circRNA_11125 and hsa_circrna_6826 may be potential new biomarkers for the diagnosis and treatment of ESCC. However, further studies are needed to clarify their specific regulatory mechanisms. Keywords: Circular RNA (circrna); microarray; esophageal squamous cell carcinoma (ESCC); microrna; pathogenesis Submitted May 22, 217. Accepted for publication Aug 3, 217. doi: /tcr View this article at:

2 Translational Cancer Research, Vol 6, No 6 December Introduction Esophageal carcinoma is the eighth most prevalent and sixth most fatal malignancy worldwide (1). In China, esophageal carcinoma is the fourth most common cancer with a much higher incidence rate than in Western countries (2). Esophageal adenocarcinoma (EAC) and squamous cell carcinoma (ESCC) are the two main types of esophageal carcinoma. Unlike Western countries, where EAC is rapidly increasing in incidence and is the dominating type (3), ESCC is the most common type of esophageal cancer in China (2). It is well known that the predilection site and biological behavior differ between ESCC and EAC, indicating that a distinctive molecular biological mechanism is involved in the development and metastasis of ESCC. Many controversies exist with respect to therapy of ESCC, including the extent of lymphadenectomy () and the clinical value of preoperative chemoradiotherapy or chemotherapy alone (5). These unclear issues in clinical practice are the manifestation of an incomplete understanding of the molecular and genetic basis of ESCC and may partially be responsible for the poor prognosis of ESCC at present. The development of new biomarkers that could act as diagnostic indicators, therapeutic targets, or prognosis predictors is eagerly anticipated to improve the diagnosis and treatment of ESCC. Circular RNAs (circrnas) are an emerging class of endogenous noncoding RNAs characterized by a particular covalently closed-loop molecular structure resulting from a 3 to 5 end joining that is stable and resistant to degradation. circrnas were first discovered in eukaryotic cells in 1979 by Hsu and Coca-Prados using electron microscopy (6); however, research on circrna in the following decades has been slow due to limited understanding and lack of specific detection technology. In the past few years, the introduction of high-throughput RNA sequencing technologies has made it possible to directly detect circrna sequences in the whole genome, which has shed new light on circrna studies. Recent studies revealed that circrnas are widely involved in a variety of diseases such as neurodegenerative diseases (7), hematological malignancies (8), and gastric cancer (9) through mirna sponging or other regulatory roles (1,11). At present, there are only a small number of studies reporting the function of circrna in ESCC (12-1), and the specific molecular mechanism by which circrnas regulate ESCC is still unclear. In this study, we performed a circrna microarray analysis to identify differentially Table 1 Patients characteristics Patient s number Age (years) Gender Staging Patient 1 51 Female IIIa Patient 2 58 Male IIIa Patient 3 56 Female IIIc Patient 5 Male IIIa Patient 5 58 Male IIIa expressed circrnas between ESCC and adjacent nonneoplastic tissues and then explored their underlying function by prediction of circrna/mirna interactions. Our results suggest that multiple circrnas may be involved in the pathogenesis of ESCC. Methods Patients and sample collection ESCC specimens were obtained from 5 patients (3 males and 2 females with age ranging from 51 to 58 years) with postoperative pathologic stage III disease (IIIa in and IIIc in 1 patient) who underwent minimally invasive esophagectomy and three-field lymph node dissection between May 216 and June 216 at Fujian Union Hospital (Table 1). The control group consisted of adjacent nonneoplastic tissues collected from the same patients. All samples were preserved in liquid nitrogen immediately after resection and then transferred to the Institute of Cardiothoracic Surgery of Fujian Union Hospital for storage at 8. RNA extraction and sample quality control Total RNA was extracted from five pairs of tumor and adjacent non-neoplastic tissues using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer s instructions. The purity and concentration of total RNA samples were measured and quantified by NanoDrop ND-1 (NanoDrop Technologies/Thermo Scientific, Wilmington, DE, USA). The integrity of RNA was tested by electrophoresis on a denaturing agarose gel. RNA labeling and hybridization We completed RNA labeling and array hybridization

3 125 Chen et al. Altered expression of circrnas in ESCC progression following the manufacturer s protocol (Arraystar Inc., Rockville, MD, USA). Extracted total RNAs were treated with RNase R (Epicentre, Madison, WI, USA) so that linear RNAs were removed and circular RNAs were enriched. The enriched circular RNAs were then amplified and transcribed into fluorescent crna with random primers following the protocol of Arraystar Super RNA Labeling Kit. RNeasy Mini Kit (Qiagen, Hilden, Germany) was used to purify the labeled crnas (pmol Cy3/μg crna) and NanoDrop ND-1 was used to determine the concentration and specific activity of the labeled crnas in order to assess labeling efficiency. Next, 5 µl of 1 blocking agent and 1 μl of 25 fragmentation buffer were added into 1 µg of each labeled crna sample, followed by heating the mixture at 6 for 3 min to fragment the labeled crna. Finally, the labeled crna samples were diluted by adding 25 μl of 2 hybridization buffer. A 5 μl volume of the hybridized solution was then dispensed into a gasket slide, which was assembled with a circrna expression microarray slide. After incubation at 65 for 17 hours in an Agilent hybridization oven, the hybridized arrays were washed and fixed and then scanned by the Agilent Scanner to generate circrnas expression profiles for microarray data analysis. Microarray data analysis Raw data extraction was performed after the scanned images were imported into Agilent Feature Extraction software. Quantile normalization of the raw data and subsequent data processing were performed using the limma package in R software. Low intensity filtering was then performed, and the circrnas with flags in Present or Marginal ( All Targets Value ) in at least 5 out of the 1 samples were selected for further analyses. To compare differences in circrnas expression profile between the ESCC and adjacent nontumor tissue groups, we calculated the fold change (i.e., the ratio of the group averages) between the groups for each circrna. t-test was used to estimate statistical significance of the differences. Significant differential expression of circrnas was defined by fold change >1.5 and P value <.5 in this study. We selected Data/Sort & Filter function in Microsoft Excel to filter the acquired data and rank the differentially expressed circrnas based on parameters such as fold change and P value. qrt-pcr validation of candidate circrnas Real-time quantitative reverse transcription-polymerase chain reaction (qrt-pcr) was performed to validate the differentially expressed circrnas between ESCC tissues and control tissues. The extracted total RNA was reverse transcribed into cdna using Super Script III Reverse Transcriptase (Invitrogen) following a standard protocol. The relative expression of circrnas was determined using the ViiA 7 Real-time PCR System (Applied Biosystems, Foster City, CA, USA). circrna levels were normalized using β-actin as the internal control. The data were calculated by the 2 ΔΔCt method, and quantitative PCR was performed in triplicate. The specific primers used in this study are listed in Table 2. Prediction of circrna/mirna interaction An in-house mirna target prediction software from Arraystar, which was based on TargetScan (15) & miranda (16) applications and databases, was used to facilitate the prediction of circrna/microrna interaction, and the differentially expressed circrnas between groups were annotated in detail using the circrna/mirna interaction information. Statistical analyses All data are presented as the mean ± standard deviation (SD). Student s t-test was used or comparisons between groups. A P value of <.5 was considered statistically significant. Results Differentially expressed circrnas based on microarray analysis In this study, in total, 12,652 circrnas were detected by the Arraystar Human circrna Array. We used a box plot to examine and compare the distributions of expression values for the samples after normalization. As shown in Figure 1, the distribution of circrnas was similar among all tested samples. Hierarchical clustering was performed to analyze circrna expression in order to formulate hypotheses about the relationships among samples. The hierarchical clustering showed distinguishable circrna expression profiles among the samples (Figure 2). A scatter plot was used to facilitate assessment of the variation in circrna expression between the ESCC and non-neoplastic tissue groups (Figure 3). Volcano plots were then used to determine differential expression between the two

4 Translational Cancer Research, Vol 6, No 6 December Table 2 Primers used in this study for qrt-pcr validation Primer name Primer sequences Annealing temperature ( ) Length (bp) β -actin (H) hsa_circrna_58 hsa_circrna_198 hsa_circrna_11125 hsa_circrna_11 hsa_circrna_11839 hsa_circrna_13836 F: 5' GTGGCCGAGGACTTTGATTG 3' 6 73 R: 5' CCTGTAACAACGCATCTCATATT 3' F: 5' CAACACCACGCCTGGCTTAT 3' R: 5' GGTGCTGACTTGCCCTCTGT 3' F: 5' GCTCCTTGACCAGTCTGGTG 3' 6 89 R: 5' GAGGGTCTGCGTGGTGTATT 3' F: 5' TCCATAACCAAGCTGGAGTG 3' R: 5' GATGATGGACTGTAGGAGCG 3' F: 5' GGGAGGATTTCTCAGGCTATG 3' 6 12 R: 5' TCTCCGACAGGGAGGTGTC 3' F: 5' CCATTTATGAGGAGGTGGCTT 3' 6 8 R: 5' TTCCACTTGTCTGCCTCGG 3' F: 5' AACAGCGGATCCCTGTATTA 3' R: 5' GCCACAGCAATCTCCTCAT 3 F: 5' ACTATGATGCCTAAACCACCT 3' hsa_circrna_6826 R: 5' AATAGCCTTCTAAGAACCTGAC 3' qrt-pcr, quantitative reverse transcription-polymerase chain reaction. Normalized intensity values A36 A37 A38 A39 A2 B36 B37 B38 B39 B2 Samples Figure 1 Box plot of circrna distribution in all samples. A box plot was introduced into this study to inspect and compare the distribution of expression values of all samples after normalization. The distribution of circrnas was similar among all tested samples. groups, with red dots representing differentially expressed circrnas that reached statistical significance (Figure ). We identified 267 circrnas that were differentially expressed in ESCC compared with non-neoplastic tissues, including 92 upregulated and 175 downregulated circrnas (fold change >1.5, P value <.5). The differentially expressed circrnas are categorized and summarized in Figures 5 and 6. Validation of selected circrnas using qrt-pcr We randomly selected seven significantly differentially expressed circrnas for validation in all samples by qrt- PCR, and among them, five circrnas exhibited expression patterns consistent with the microarray data. These five circrnas included two upregulated (hsa_circrna_11125 and hsa_circrna_6826) and three downregulated (hsa_circrna_11, hsa_circrna_13836 and hsa_ circrna_11839) circrnas (Table 3). The results of qrt- PCR validation are shown in Figure 7.

5 1252 Chen et al. Altered expression of circrnas in ESCC progression Count Color key and histogram 6 8 Value Group-T (normalized) Group-C (normalized) [A36, T] [B39, C] [A39, T] [B36, C] Figure 2 Hierarchical clustering for analysis of circrna expression among samples. circrna expression was analyzed by hierarchical clustering in order to formulate hypotheses about the relationships among samples. The results of hierarchical clustering revealed distinguishable circrna expression profiles among the samples. In this plot, each column represents the expression profile of a sample, where A represents tumor tissues and B represents the adjacent non-neoplastic tissues. Each row represents a particular circrna. Red denotes higher expression level, and [A2, T] green denotes a lower expression level. [B37, C] [B2,C] Prediction of circrna/microrna interactions According to the cerna hypothesis and a previous report (1), circrnas may interact with mirnas through mirna response elements (MREs) and thus function as mirna sponges. Therefore, we used an in-house mirna target prediction software from Arraystar to predict mirnas that may bind to the selected circrnas. The information on circrna/mirna interactions for the five confirmed circrnas including the most likely potential targeted mirnas is summarized in Figure 8. [B38, C] [A38, T] [A37, T] Figure 3 Scatter plot for the assessment of variation in circrna expression between groups. We used a scatter plot to visualize the variation in circrna expression between the ESCC and nonneoplastic tissue groups. The x-axis represents adjacent non-tumor tissue group and the y-axis represents ESCC group. The green line in the middle represents no difference between groups, the upper and lower green lines refer to 1.5-fold changes. The circrnas above the upper and below the lower green lines indicate >1.5-fold differences between the two compared groups. Discussion In the classic gene expression model, known as the central dogma, the expression and adjustment of genetic and functional information within the genome are mediated by linear RNAs. The emergence of circrnas represented both a challenge and a supplement to the central dogma. Recent studies have shown that circrnas are highly abundant in the cytoplasm of eukaryotic cells, at levels comparable to or even exceeding those of their canonical linear isoforms (17,18). Moreover, circrnas are structurally stable, highly evolutionarily conserved, and tissue specific, with multiple regulatory functions (18-22). The mechanism by which circrnas exert regulatory functions is far from clear. Exonic circrnas, the most abundant type, have been found to function as mirna sponges. cirs-7, one of the best known circrnas, contains more than 7 MREs. cirs-7 can specifically bind to mir-7 and strongly suppress mir-

6 Translational Cancer Research, Vol 6, No 6 December Up-regulated Down-regulated Total -log1 (P value) Figure Visualization of differential expression of circrnas between groups by volcano plots. The red dots in the plot represents circrnas with statistically significant altered expression (fold change >1.5 and P<.5). Log2 (fold change) T vs. C Count Figure 5 Chromosomal distribution of the differentially expressed circrnas. chr1 chr2 chr3 Count exonic intronic chr chr5 chr6 chr7 chr8 chr9 chr1 chr11 chr12 chr13 chr1 chr15 chr16 chr17 chr18 chr19 antisense Down-regulated Up-regulated sense overlapping intergenic chr2 chr21 chr22 chrx chry Figure 6 Classification of the differentially expressed circrnas. Table 3 Biological information on the 5 differentially expressed circrnas validated by qrt-pcr CircRNA P value FDR Fold change Regulation Chrom CircRNA type Strand Best transcript Gene symbol hsa_circrna_ Up chr12 exonic + NM_1837 DRAM1 hsa_circrna_ Up chr6 exonic + NM_185 FIG hsa_circrna_ Down chr12 exonic NM_873 IFFO1 hsa_circrna_ Down chr16 exonic + NM_3983 SLC7A6 hsa_circrna_ Down chr5 exonic + NM_2615 PARP8 + represents sense strand; represents antisense strand. qrt-pcr, quantitative reverse transcription-polymerase chain reaction. 7 activity (1,23). Likewise, the sex-determining region Y (SRY) serves as a mir-138 sponge (1,2). In the past few years, hsa_circ_1569 (25), circpvt1 (26), circtcf25 (27) and cir-itch (12) were discovered as new circrnas with mirna sponge function. These circrnas have been shown to be involved in the development of several kinds of carcinoma by sequestering tumor-related mirnas. In addition, exonic circrnas can also function by regulating transcription (28) and interacting with RNA binding proteins (29), but elucidation of specific mechanisms requires further investigation. For other circrna types, such as intronic circrnas and exon-intron circrnas

7 125 Chen et al. Altered expression of circrnas in ESCC progression A Hsa_circRNA_11 B Hsa_circRNA_11839 C Hsa_circRNA_13836 D Hsa_circRNA_11125 E Hsa_circRNA_6826 F Hsa_circRNA_198 G Hsa_circRNA_58 Figure 7 qrt-pcr validation of the selected circrnas identified by the microarray. (A) hsa_circrna_11; (B) hsa_circrna_11839; (C) hsa_circrna_13836; (D) hsa_circrna_11125; (E) hsa_circrna_6826; (F) hsa_circrna_198; (G) hsa_circrna_58. * represented the relatively expression level of the targeted circrna in cancerous tissues. (EIciRNAs), there have only been a few relevant studies. Existing reports reveal that these circrnas regulate transcription by enhancing the expression of their parental gene (11,3). The characteristics mentioned above qualify circrnas as a promising target for the diagnosis and treatment of diseases. There are very few studies on circrnas in ESCC. Li et al. (12) discovered that cir-itch was expressed at low levels in ESCC compared with peritumoral tissues. Furthermore, it was shown to function as a sponge for mir-7, mir-17, and mir-21, subsequently increasing the level of ITCH and thus playing an inhibitory role in ESCC by regulating the Wnt pathway. Su et al. (13) performed circrna microarray and bioinformatics analyses to compare expression profiles of circrna in radioresistant and non-radioresistant ESCC cells. They reported a comprehensive expression and functional profile of differentially expressed circrnas in radioresistant esophageal cancer cells, among which circrna_159 and circrna_167 were identified as the two largest nodes in the circrna/microrna co-expression network. Xia et al. (1) found that hsa_circ_6793 was upregulated in ESCC tumor tissues, and showed that in vitro silencing of hsa_circ_6793 resulted in the inhibition of proliferation and migration of ESCC cells and cell cycle arrest. This suggested that circ_6793 has potential as a novel biomarker and therapeutic target of ESCC. Although the specific regulatory mechanism of circrna in ESCC was unclear, the inspiring results of these studies confirmed that circrnas play a role in the process of ESCC pathogenesis. In this study, we compared circrna expression profiles between ESCC and adjacent non-neoplastic tissues using Arraystar Human Circular RNA Microarrays to investigate the mechanism of ESCC pathogenesis. Multiple differentially expressed circrnas were observed and validated in the ESCC tissues compared with the adjacent non-neoplastic tissues. Our results revealed that these dysregulated circrnas might be involved in the process of ESCC pathogenesis, especially in the case of hsa_ circrna_11125 and hsa_circrna_6826, which showed significantly upregulated expression in the ESCC tissues. According to the prediction results for circrna/microrna interactions, hsa_circrna_11125 has a potential binding site for mir-13-3p and thus might function as a mir-

8 Translational Cancer Research, Vol 6, No 6 December A B C D E Figure 8 Annotation of circrna/microrna interactions. (A) hsa_circrna_11125 vs. hsa-mir-13-3p; (B) hsa_circrna_6826 vs. hsamir-23a-3p; (C) hsa_circrna_11 vs. hsa-mir-15-5p; (D) hsa_circrna_11839 vs. hsa-mir-13-3p; (E) hsa_circrna_13836 vs. hsa-mir-3a-3p. 13-3p sponge. mir-23a-3p was a predicted target of hsa_circrna_6826, which might sequester mir-23a- 3p through mirna response elements. According to previous reports, both mir-13-3p and mir-23a-3p are downregulated in ESCC tissues (31,32), and further in vivo and in vitro studies have provided strong evidence that both mirnas act as tumor suppressors in ESCC. Enforced expression of mir-13-3p and mir-23a-3p inhibited growth, proliferation, migration and invasion of ESCC cells through different mechanisms, while their reduced expression was associated with poor patient survival (33-3). Specific binding of hsa_circrna_11125 and mir- 13-3p or that of hsa_circrna_6826 and mirna- 23a-3p might relieve the suppression of tumor-related

9 1256 Chen et al. Altered expression of circrnas in ESCC progression genes by mirnas, thus promoting the occurrence and development of ESCC. Regarding the three circrnas with downregulated expression, we believe that mirna sponging may not be their main function in ESCC pathogenesis, and the mechanism associated with their low expression level remains to be clarified. Conclusions In summary, we detected differential circrna expression profiles between ESCC and adjacent non-neoplastic tissues. Our results demonstrated that multiple circrnas were aberrantly expressed in ESCC tissues and might be involved in the process of ESCC pathogenesis. In particular, hsa_circrna_11125 and hsa_circrna_6826 were significantly upregulated in ESCC, and these circrnas might serve as potential diagnostic or therapeutic markers in ESCC. Further studies are needed to clarify their specific regulatory mechanisms. Acknowledgements Funding: This work was supported by the Science and Technology Key Project of Fujian Province, China (Grant Number: 21Y2) and Youth Fund of Fujian Health Department (Grant Number: ). Footnote Conflicts of Interest: The authors have no conflicts of interest to declare. Ethical Statement: The study was approved by the ethics committee of Fujian Medical University Union Hospital (No. 216ky12), and written informed consent was obtained from all patients. The experiments were performed according to the Ethical Guidelines for Human Genome/ Gene Research issued by the Chinese Government. References 1. Hongo M, Nagasaki Y, Shoji T. Epidemiology of esophageal cancer: Orient to Occident. Effects of chronology, geography and ethnicity. J Gastroenterol Hepatol 29;2: Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 215. CA Cancer J Clin 216;66: Siegel RL, Miller KD, Jemal A. Cancer statistics, 216. CA-Cancer J Clin 216;66:7-3.. Ma GW, Situ DR, Ma QL, et al. Three-field vs twofield lymph node dissection for esophageal cancer:a metaanalysis. World J Gastroenterol 21;2: Kumagai K, Rouvelas I, Tsai JA, et al. Meta-analysis of postoperative morbidity and perioperative mortality in patients receiving neoadjuvant chemotherapy or chemoradiotherapy for resectable oesophageal and gastro-oesophageal junctional cancers. Brit J Surg 21;11: Hsu MT, Coca-Prados M. Electron microscopic evidence for the circular form of RNA in the cytoplasm of eukaryotic cells. Nature 1979;28: Kumar L, Shamsuzzama, Haque R, et al. Circular RNAs: the Emerging Class of Non-coding RNAs and Their Potential Role in Human Neurodegenerative Diseases. Mol Neurobiol 216. [Epub ahead of print]. 8. Bonizzato A, Gaffo E, Te Kronnie G, et al. CircRNAs in hematopoiesis and hematological malignancies. Blood Cancer J 216;6:e Li P, Chen S, Chen H, et al. Using circular RNA as a novel type of biomarker in the screening of gastric cancer. Clin Chim Acta 215;: Hansen TB, Jensen TI, Clausen BH, et al. Natural RNA circles function as efficient microrna sponges. Nature 213;95: Li Z, Huang C, Bao C, et al. Exon-intron circular RNAs regulate transcription in the nucleus. Nat Struct Mol Biol 215;22: Li F, Zhang L, Li W, et al. Circular RNA ITCH has inhibitory effect on ESCC by suppressing the Wnt/betacatenin pathway. Oncotarget 215;6: Su H, Lin F, Deng X, et al. Profiling and bioinformatics analyses reveal differential circular RNA expression in radioresistant esophageal cancer cells. J Transl Med 216;1: Xia W, Qiu M, Chen R, et al. Circular RNA has_ circ_6793 is upregulated in esophageal squamous cell carcinoma and promoted proliferation. Sci Rep 216;6: Enright AJ, John B, Gaul U, et al. MicroRNA targets in Drosophila. Genome Biol 23;5:R Pasquinelli AE. MicroRNAs and their targets: recognition, regulation and an emerging reciprocal relationship. Nat Rev Genet 212;13: Salzman J, Gawad C, Wang PL, et al. Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types. Plos One

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